Guidelines for Dealing with Thai Baht Volatility in Industrial Businesses

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Since Thailand switched the exchange rate to a floating system in 1997, Thai industries engaged in import and export activities have experienced profits and losses due to fluctuating exchange rates continuously. The objective of this research was to investigate strategies for managing the volatility of the Thai baht in the industrial sector and to develop a structural equation model based on these findings. The study employed a combination of qualitative and quantitative approaches. In-depth interviews were conducted with 9 experts to develop the quantitative research tools, and a group discussion involving 11 experts was held to establish a consensus on the study’s model. As for the quantitative study, the data were collected from 500 industrial business executives awarded PRIME MINISTER’S EXPORT AWARD, using the developed questionnaires. Descriptive, referential, and multiple statistics were employed to analyze the data. The study revealed that 4 major guideline areas for handling the volatility of the baht in the industrial sector were found, prioritized as follows: risk control (x ̅ = 4.36), resource-centered (x ̅ = 4.34), analysis of the environment (x ̅ = 4.31), and innovation and technology (x ̅ = 4.30) respectively. The most important guideline item in each area was: always have the policy to review profit and loss from exchange rates, select personnel with financial and language skills to analyze and forecast the volatility of the baht, continuously analyze the GDP of the major currency country, connect the demand for products to digital technology and corporate partners, and respectively. As for the hypothesis testing results, it was found that small and medium-sized businesses, and large businesses differently prioritized guidelines to cope with baht volatility in the industrial sector at the statistical significance level of 0.05. The analysis of the developed structural equation model revealed that the evaluation criteria were consistent with the empirical data with its Chi-square Probability, the Relative Chi-square, Goodness of Fit Index, and the Root Mean Square Error of Approximation of 0.055, 1.148, 0.964, and 0.017, respectively.